Part 1: RNA

Load RNA samples

Out of 30 samples, we selected 17 for this study. These are the normal tissue samples form the control, the UVA and the UVA+SFN treatment groups. normal tissue samples from the UVB_UA groups as well as tumor samples were excluded from this analysis. Additionally, one of the control samples at Week 2 (baseline) was removed after outlier analysis.
7,219 genes with zero counts in > 80% (> 13 out of 18) of samples were removed. 17,202 out of 24,421 genes were left.

[1] 7219
[1] 17202

Transcripts per kilobase million (TPM) normalization

Next, we noramized the counts. To convert number of hits to the relative abundane of genes in each sample, we used transcripts per kilobase million (TPM) normalization, which is as following for the j-th sample:
1. normilize for gene length: a[i, j] = 1,000*count[i, j]/gene[i, j] length(bp)
2. normalize for seq depth (i.e. total count): a(i, j)/sum(a[, j])
3. multiply by one million
A very good comparison of normalization techniques can be found at the following video:
RPKM, FPKM and TPM, clearly explained

After the normalization, each sample’s total is 1M:

02w_CON_0 02w_SFN_0 02w_SFN_1 02w_UVB_0 02w_UVB_1 15w_CON_0 15w_CON_1 15w_SFN_0 15w_SFN_1 
    1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06 
15w_UVB_0 15w_UVB_1 25w_CON_0 25w_CON_1 25w_SFN_0 25w_SFN_1 25w_UVB_0 25w_UVB_1 
    1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06     1e+06 

Color Legend:
YELLOW: TMP > 10
RED: TMP > 100

Top 100 most abundant RNA molecules

# Separate top 100 abundant genes
tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[(nrow(tpm) - 99):nrow(tpm)]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p2 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p2)

Bottom 100 least abundant RNA molecules

tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[1:100]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p3 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p3)

Meta data

dmeta <- data.table(Sample = colnames(dt1)[-c(1:2)])

dmeta$time <- substr(x = dmeta$Sample,
                     start = 1,
                     stop = 3)
dmeta$time <- factor(dmeta$time,
                     levels = c("02w",
                                "15w",
                                "25w"))
dmeta$Week <- factor(dmeta$time,
                     levels = c("02w",
                                "15w",
                                "25w"),
                     labels = c("Week 2",
                                "Week 15",
                                "Week 25"))

dmeta$trt <- substr(x = dmeta$Sample,
                    start = 5,
                    stop = 7)
dmeta$trt <- factor(dmeta$trt,
                    levels = c("CON", 
                               "UVB",
                               "SFN"))
dmeta$Treatment <- factor(dmeta$trt,
                          levels = c("CON", 
                                     "UVB",
                                     "SFN"),
                          labels = c("Negative Control",
                                     "Positive Control (UVB)",
                                     "Sulforaphane (SFN)"))

dmeta$Replica <- substr(x = dmeta$Sample,
                        start = 9,
                        stop = 9)
dmeta$Replica <- factor(dmeta$Replica,
                        levels = 0:1)

datatable(dmeta,
          rownames = FALSE,
          class = "cell-border stripe",
          options = list(pageLength = nrow(dmeta)))

PCA of TPM

NOTE: the distributions are skewed. To make them symmetric, log transformation is often applied. However, there is an issue of zeros. In this instance, we added a small values lambda[i] equal to 1/10 of the smallest non-zero value of i-th gene.

dm.tpm <- as.matrix(tpm[, -c(1:2), with = FALSE])
rownames(dm.tpm) <- tpm$Geneid

# # Remove 02w_CON_1 sample and redo PCA
# dm.tpm <- dm.tpm[, colnames(dm.tpm) != "02w_CON_1"]
# dmeta <- dmeta[dmeta$Sample != "02w_CON_1", ]

# Add lambdas to all values, then take a log
dm.ltpm <- t(apply(X = dm.tpm,
                      MARGIN = 1,
                      FUN = function(a) {
                        lambda <- min(a[a > 0])/10
                        log(a + lambda)
                      }))

# PCA----
m1 <- prcomp(t(dm.ltpm),
             center = TRUE,
             scale. = TRUE)

s1 <- summary(m1)
s1
Importance of components:
                           PC1     PC2     PC3      PC4      PC5      PC6      PC7
Standard deviation     66.5041 61.8206 45.2845 30.42909 28.24422 26.84136 25.01865
Proportion of Variance  0.2571  0.2222  0.1192  0.05383  0.04637  0.04188  0.03639
Cumulative Proportion   0.2571  0.4793  0.5985  0.65232  0.69869  0.74058  0.77696
                            PC8      PC9     PC10     PC11    PC12     PC13     PC14
Standard deviation     23.05989 22.08373 21.24391 20.87624 20.6980 20.28169 19.42403
Proportion of Variance  0.03091  0.02835  0.02624  0.02534  0.0249  0.02391  0.02193
Cumulative Proportion   0.80788  0.83623  0.86246  0.88780  0.9127  0.93662  0.95855
                           PC15     PC16      PC17
Standard deviation     19.14803 18.61200 2.085e-13
Proportion of Variance  0.02131  0.02014 0.000e+00
Cumulative Proportion   0.97986  1.00000 1.000e+00

Pareto chart of variance explained by principal components

imp <- data.table(PC = colnames(s1$importance),
                  Variance = 100*s1$importance[2, ],
                  Cumulative = 100*s1$importance[3, ])
imp$PC <- factor(imp$PC,
                 levels = imp$PC)
p1 <- ggplot(imp,
             aes(x = PC,
                 y = Variance)) +
  geom_bar(stat = "identity",
           fill = "grey",
           color = "black") +
  geom_line(aes(y = rescale(Cumulative,
                            to = c(min(Cumulative)*max(imp$Variance)/100,
                                   max(imp$Variance))),
                group = rep(1, nrow(imp)))) +
  geom_point(aes(y = rescale(Cumulative,
                             to = c(min(Cumulative)*max(imp$Variance)/100,
                                    max(imp$Variance))))) +
  scale_y_continuous("% Variance Explained",
                     breaks = seq(from = 0, 
                                  to = max(imp$Variance), 
                                  by = 5),
                     labels = paste(seq(from = 0, 
                                        to = max(imp$Variance),
                                        by = 5),
                                    "%",
                                    sep = ""),
                     sec.axis = sec_axis(trans = ~.,
                                         name = "% Cumulative Variance",
                                         breaks = seq(from = 0, 
                                                      to = max(imp$Variance), 
                                                      length.out = 5),
                                         labels = paste(seq(from = 0, 
                                                            to = 100, 
                                                            length.out = 5),
                                                        "%",
                                                        sep = ""))) +
  scale_x_discrete("") +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))

# Save for publication
tiff(filename = "tmp/pca_pareto.tiff",
     height = 6,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

print(p1)

First 3 principal components, pairwise

# Biplot while keep only the most important variables (Javier)----
# Select PC-s to pliot (PC1 & PC2)
choices <- c(1:3)

# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variables
dt.scr$trt <- dmeta$trt
dt.scr$time <- dmeta$time
dt.scr$sample <- dmeta$Sample

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[choices], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))

p1 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC2,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  theme(legend.position = "none")
ggplotly(p1)


p2 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[3]) +
  theme(legend.position = "none")
ggplotly(p2)


p3 <- ggplot(data = dt.scr,
             aes(x = PC2,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[2]) +
  scale_y_continuous(u.axis.labs[3]) +
  theme(legend.position = "none")
ggplotly(p3)


# Legend only
tmp <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC2,
                 color = trt,
                 shape = time)) +
  geom_point() +
  scale_color_discrete("Treatment") +
  scale_shape_discrete("Week")
p4 <- as_ggplot(get_legend(tmp))

# Save for publication
tiff(filename = "tmp/pca.tiff",
     height = 7,
     width = 9,
     units = 'in',
     res = 600,
     compression = "lzw+p")
grid.arrange(p1, p2, p3, p4, 
             nrow = 2)
graphics.off()

First 3 principal components, 3D

scatterplot3js(x = dt.scr$PC1, 
               y = dt.scr$PC2, 
               z = dt.scr$PC3, 
               color = as.numeric(dt.scr$trt),
               renderer = "auto",
               pch = dt.scr$sample,
               size = 0.1)

Differential expression analysis (DESeq2 pipeline)

Sources:
1. Analyzing RNA-seq data with DESeq2:Interactions
2. Bioconductor Question: DESeq2 time series analysis
We are testing a model with time*treatment interaction. The idea here is to find genes with significant interaction term. That would suggest that the gene expressiondifferences between the treatments depended on time. THere are several possible scenarios:
a. No difference between the negative control and the positive control groups at baseline, significant difference at the later time point. This will show the effect of the disease (UVB radiation, in this case).
b. Significant difference between the control groups at baseline, no difference at the later time point. Same as (a) above.
c. Differences between the positive control and the SFN-treated groups. Here, we are interested in the reversal of UVB effect. Again, the interaction term will need to be significant for the reasons described above.

# Relevel: make all comparisons with the positive control (UVB)
dmeta$trt <- factor(dmeta$trt,
                    levels = c("UVB",
                               "CON",
                               "SFN"))

dtm<- as.matrix(dt1[, dmeta$Sample,
                    with = FALSE])
rownames(dtm) <- dt1$Geneid

dds <- DESeqDataSetFromMatrix(countData = dtm, 
                              colData = dmeta,
                              ~ time + trt + time:trt)
# If all samples contain zeros, geometric means cannot be
# estimated. Change default 'type = "ratio"' to 'type = "poscounts"'.
# Type '?DESeq2::estimateSizeFactors' for more details.
dds <- estimateSizeFactors(object = dds,
                           type = "poscounts")

# Run DESeq----
dds <- DESeq(object = dds,
             # test = "LRT",
             # reduced = ~ time + trt,
             fitType = "local",
             sfType = "ratio",
             parallel = FALSE)
using pre-existing size factors
estimating dispersions
gene-wise dispersion estimates
mean-dispersion relationship
final dispersion estimates
fitting model and testing
# NOTE (from DESeq help file, section Value):
# A DESeqDataSet object with results stored as metadata columns. 
# These results should accessed by calling the results function. 
# By default this will return the log2 fold changes and p-values
# for the last variable in the design formula. 
# See results for how to access results for other variables.
# In this case, the last term is the interaction term trt:time

# NOTE: 
# Likelihood ratio test (LRT) (chi-squared test) for GLM will only return 
# the results for the difference between the full and the reduced model

resultsNames(dds)
[1] "Intercept"       "time_15w_vs_02w" "time_25w_vs_02w" "trt_CON_vs_UVB" 
[5] "trt_SFN_vs_UVB"  "time15w.trtCON"  "time25w.trtCON"  "time15w.trtSFN" 
[9] "time25w.trtSFN" 
# Model matrix
mm1 <- model.matrix(~ time + trt + time:trt, dmeta)
mm1
   (Intercept) time15w time25w trtCON trtSFN time15w:trtCON time25w:trtCON time15w:trtSFN
1            1       0       0      1      0              0              0              0
2            1       0       0      0      1              0              0              0
3            1       0       0      0      1              0              0              0
4            1       0       0      0      0              0              0              0
5            1       0       0      0      0              0              0              0
6            1       1       0      1      0              1              0              0
7            1       1       0      1      0              1              0              0
8            1       1       0      0      1              0              0              1
9            1       1       0      0      1              0              0              1
10           1       1       0      0      0              0              0              0
11           1       1       0      0      0              0              0              0
12           1       0       1      1      0              0              1              0
13           1       0       1      1      0              0              1              0
14           1       0       1      0      1              0              0              0
15           1       0       1      0      1              0              0              0
16           1       0       1      0      0              0              0              0
17           1       0       1      0      0              0              0              0
   time25w:trtSFN
1               0
2               0
3               0
4               0
5               0
6               0
7               0
8               0
9               0
10              0
11              0
12              0
13              0
14              1
15              1
16              0
17              0
attr(,"assign")
[1] 0 1 1 2 2 3 3 3 3
attr(,"contrasts")
attr(,"contrasts")$time
[1] "contr.treatment"

attr(,"contrasts")$trt
[1] "contr.treatment"
head(mcols(dds))
DataFrame with 6 rows and 50 columns
                 baseMean           baseVar   allZero         dispGeneEst dispGeneIter
                <numeric>         <numeric> <logical>           <numeric>    <numeric>
Xkr4    0.414423785139076 0.750734393874421     FALSE               1e-08            1
Mrpl15   497.506315418383  6139.21631388383     FALSE 0.00292023552394721            6
Lypla1   1316.42450437205   94053.122870121     FALSE 0.00514177871417793           10
Tcea1    362.833336721312  2447.08771392985     FALSE               1e-08           20
Rgs20    412.785226796461  8337.26279018443     FALSE  0.0222228623148068            8
Atp6v1h  1163.12136188358  26870.2895984056     FALSE 0.00473653527254895            9
                    dispFit          dispersion  dispIter dispOutlier             dispMAP
                  <numeric>           <numeric> <integer>   <logical>           <numeric>
Xkr4       2.35686251255345    6.43661011051539         8       FALSE    6.43661011051539
Mrpl15  0.00975181583387631  0.0060101698743299         8       FALSE  0.0060101698743299
Lypla1   0.0074100485818535 0.00604102606581283         9       FALSE 0.00604102606581283
Tcea1    0.0123515065189161 0.00715812241817593         7       FALSE 0.00715812241817593
Rgs20    0.0111228088946145  0.0168637514204584        11       FALSE  0.0168637514204584
Atp6v1h 0.00743062379729061 0.00580961463958366         9       FALSE 0.00580961463958366
               Intercept    time_15w_vs_02w     time_25w_vs_02w      trt_CON_vs_UVB
               <numeric>          <numeric>           <numeric>           <numeric>
Xkr4     -2.359805612164 -0.228477588168501  -0.165844507463528  0.0598562849180821
Mrpl15  9.06594448953328 -0.137408907813809 -0.0412786898053219  -0.308591258014163
Lypla1  10.7337301130648 -0.629677974788472  -0.599280178188303  -0.305684497430534
Tcea1   8.78214921631808 -0.516217579095005  -0.446190172830842  -0.196562316500229
Rgs20   8.98928399842352 -0.547987096260501   -0.45980987283847 -0.0685634893160301
Atp6v1h 10.4068496272689 -0.491695240290437  -0.365919358337453   -0.17807000833384
             trt_SFN_vs_UVB      time15w.trtCON     time25w.trtCON     time15w.trtSFN
                  <numeric>           <numeric>          <numeric>          <numeric>
Xkr4        1.6582080718198    2.45478731530058   3.43262855563513  -1.67076473658365
Mrpl15    0.199168294921519  0.0156586240981802 -0.102536901707458 -0.178149323759463
Lypla1    0.179718039995711   0.281344903276623  0.348189855674569 -0.107101147581259
Tcea1   -0.0830935380769627   0.309506757416714  0.476511703155704  0.271157924759699
Rgs20     0.113854717310148 -0.0460895727086707 -0.119888249480383   0.24522833856804
Atp6v1h  0.0799789915431519   0.246974241692442    0.3213538709111  0.171814404854682
             time25w.trtSFN       SE_Intercept SE_time_15w_vs_02w SE_time_25w_vs_02w
                  <numeric>          <numeric>          <numeric>          <numeric>
Xkr4      -1.57787360996648   2.96284694407196   4.19009832838988   4.19009832833768
Mrpl15  -0.0710688162246861 0.0911721029656416  0.128443851313393   0.12828481198956
Lypla1  -0.0334225250935585 0.0832744171626043  0.118643311639213  0.118734269568226
Tcea1     0.104295727467573 0.0997712835011623  0.143038313778663  0.142999047037131
Rgs20   -0.0021489805803274  0.140429387999271  0.199941844408924  0.199839918784151
Atp6v1h  0.0421037362927887  0.082816070681427  0.117808586333953  0.117641366800501
        SE_trt_CON_vs_UVB SE_trt_SFN_vs_UVB SE_time15w.trtCON SE_time25w.trtCON
                <numeric>         <numeric>         <numeric>         <numeric>
Xkr4     5.13171132899515  4.17888254775382  6.55361338849756   6.5053324829564
Mrpl15  0.161651924073278 0.128745553404475 0.208065870702812 0.207540172422408
Lypla1  0.145519737526484 0.117738852864755 0.188694868576427 0.188378486771387
Tcea1   0.175595965579231 0.142746566195654 0.228119616478045 0.226530264396902
Rgs20   0.244051721146729 0.198791901404911 0.317398591717306 0.316856976912824
Atp6v1h 0.144448031460997 0.117328387791251 0.187086661574303 0.186409051317023
        SE_time15w.trtSFN SE_time25w.trtSFN WaldStatistic_Intercept
                <numeric>         <numeric>               <numeric>
Xkr4     5.91776899533986   5.9177689953029      -0.796465580810876
Mrpl15  0.181401275197653 0.181107411065465         99.437702922678
Lypla1   0.16762877155602 0.167772361896891        128.895889983904
Tcea1   0.202476358816243 0.203686983950883         88.022814863515
Rgs20   0.281821816802482 0.282640507763004         64.012840378327
Atp6v1h  0.16627170341325  0.16643031647693        125.662199880281
        WaldStatistic_time_15w_vs_02w WaldStatistic_time_25w_vs_02w
                            <numeric>                     <numeric>
Xkr4              -0.0545279776897975           -0.0395800991928805
Mrpl15              -1.06979747499584            -0.321773787287314
Lypla1              -5.30731961278428              -5.0472385130895
Tcea1                -3.6089462009025             -3.12023179227889
Rgs20               -2.74073242587354             -2.30089101134551
Atp6v1h             -4.17367914845039             -3.11046503699663
        WaldStatistic_trt_CON_vs_UVB WaldStatistic_trt_SFN_vs_UVB
                           <numeric>                    <numeric>
Xkr4              0.0116640007749233            0.396806575171895
Mrpl15             -1.90898598815487             1.54699164091361
Lypla1             -2.10063942270993             1.52641235771297
Tcea1              -1.11940109701175           -0.582105337392646
Rgs20             -0.280938355992205            0.572733177284935
Atp6v1h            -1.23276175197943            0.681667864434048
        WaldStatistic_time15w.trtCON WaldStatistic_time25w.trtCON
                           <numeric>                    <numeric>
Xkr4               0.374570053157096            0.527663814974626
Mrpl15             0.075258013461256           -0.494058092516009
Lypla1              1.49100452703975             1.84835254620728
Tcea1               1.35677396882921             2.10352336110292
Rgs20             -0.145210388172487           -0.378367081099077
Atp6v1h             1.32010609208693             1.72391774240929
        WaldStatistic_time15w.trtSFN WaldStatistic_time25w.trtSFN WaldPvalue_Intercept
                           <numeric>                    <numeric>            <numeric>
Xkr4              -0.282330171708181           -0.266633187476376    0.425761473479907
Mrpl15            -0.982073161091917            -0.39241252363216                    0
Lypla1            -0.638918644974184           -0.199213533836397                    0
Tcea1               1.33920782823731            0.512039235127185                    0
Rgs20              0.870153848805504         -0.00760322926581116                    0
Atp6v1h             1.03333520573646             0.25298117064282                    0
        WaldPvalue_time_15w_vs_02w WaldPvalue_time_25w_vs_02w WaldPvalue_trt_CON_vs_UVB
                         <numeric>                  <numeric>                 <numeric>
Xkr4             0.956514518769316          0.968427893548228         0.990693684883985
Mrpl15           0.284710479117951          0.747624073744977        0.0562638992384959
Lypla1        1.11249010699918e-07       4.48241630561262e-07        0.0356726306570048
Tcea1         0.000307443353893709        0.00180708779608543         0.262969063182376
Rgs20          0.00613024072810697         0.0213977923150809         0.778757681035553
Atp6v1h       2.99719777251695e-05        0.00186793007683514         0.217664665159797
        WaldPvalue_trt_SFN_vs_UVB WaldPvalue_time15w.trtCON WaldPvalue_time25w.trtCON
                        <numeric>                 <numeric>                 <numeric>
Xkr4            0.691510101678589         0.707980248270468         0.597732692383313
Mrpl15          0.121865261334255         0.940009427107256         0.621265153192805
Lypla1          0.126907201963519         0.135960306359441        0.0645513587969476
Tcea1           0.560495730233128         0.174853041105187        0.0354200453908308
Rgs20           0.566825369916453          0.88454476429304         0.705157919128452
Atp6v1h          0.49544899193699          0.18679959906884        0.0847226938662104
        WaldPvalue_time15w.trtSFN WaldPvalue_time25w.trtSFN  betaConv  betaIter
                        <numeric>                 <numeric> <logical> <numeric>
Xkr4            0.777690352523301         0.789751600479958      TRUE        13
Mrpl15          0.326063806588436         0.694753434090283      TRUE         2
Lypla1          0.522875857997676         0.842095713129838      TRUE         2
Tcea1           0.180503024691872         0.608623550427472      TRUE         2
Rgs20           0.384216333175638         0.993933559205863      TRUE         3
Atp6v1h         0.301447057198772         0.800282763673559      TRUE         2
                deviance  maxCooks
               <numeric> <logical>
Xkr4    25.9033824686373        NA
Mrpl15  165.306361397833        NA
Lypla1  196.962147294101        NA
Tcea1   157.679951768679        NA
Rgs20   178.614721232345        NA
Atp6v1h 192.597108944526        NA

Results

Effect of UVB at Week 2

# res_con_uvb_week2 <- results(dds,
#                              contrast = c(0,0,0,1,0,0,0,0,0),
#                              alpha = 0.1)
# SAME AS:
res_con_uvb_week2 <- results(dds,
                             name = "trt_CON_vs_UVB",
                             alpha = 0.1)
res_con_uvb_week2 <- res_con_uvb_week2[order(res_con_uvb_week2$padj,
                                                   decreasing = FALSE),]
summary(res_con_uvb_week2)

out of 17202 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 1546, 9%
LFC < 0 (down)     : 1537, 8.9%
outliers [1]       : 0, 0%
low counts [2]     : 2335, 14%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
# How many adjusted p-values were less than 0.05?
sum(res_con_uvb_week2$padj < 0.1, 
    na.rm = TRUE)
[1] 3083
# MA plot
# Save for publication
tiff(filename = "tmp/ma_w2_con_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plotMA(res_con_uvb_week2,
             main = "Control vs. UVB at Week 2",
             alpha = 0.8)
graphics.off()

plotMA(res_con_uvb_week2,
             main = "Control vs. UVB at Week 2",
             alpha = 0.8)

Protective effect of SFN at Week 2

# res_sfn_uvb_week2 <- results(dds,
#                              contrast = c(0,0,0,0,1,0,0,0,0),
#                              alpha = 0.1)
# SAME AS;
res_sfn_uvb_week2 <- results(dds,
                             name = "trt_SFN_vs_UVB",
                             alpha = 0.1)
res_sfn_uvb_week2 <- res_sfn_uvb_week2[order(res_sfn_uvb_week2$padj,
                                                   decreasing = FALSE),]
summary(res_sfn_uvb_week2)

out of 17202 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 26, 0.15%
LFC < 0 (down)     : 35, 0.2%
outliers [1]       : 0, 0%
low counts [2]     : 3669, 21%
(mean count < 5)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
# How many adjusted p-values were less than 0.05?
sum(res_sfn_uvb_week2$padj < 0.1, 
    na.rm = TRUE)
[1] 61
# MA plot
# Save for publication
tiff(filename = "tmp/ma_w2_sfn_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(plotMA(res_sfn_uvb_week2,
             main = "UVB+SFN vs UVB at Week 2",
             alpha = 0.8))
NULL
graphics.off()

print(plotMA(res_sfn_uvb_week2,
             main = "UVB+SFN vs UVB at Week 2",
             alpha = 0.8))
NULL

Genes that were significantly differentiated at both comparisons at Week 2

lgene.w2.con <- unique(res_con_uvb_week2@rownames[res_con_uvb_week2$padj < 0.1])
lgene.w2.sfn <- unique(res_sfn_uvb_week2@rownames[res_sfn_uvb_week2$padj < 0.1])
lgene.w2 <- lgene.w2.con[lgene.w2.con %in% lgene.w2.sfn]
lgene.w2 <- lgene.w2 [!is.na(lgene.w2 )]
lgene.w2
 [1] "Utrn"    "Stom"    "Tesc"    "Cited4"  "Cdhr1"   "Slc7a11" "Mki67"   "Cyp26b1"
 [9] "Smc2"    "Mad2l1"  "Slc4a7"  "Ankrd23" "Ifitm3"  "Etv3"    "Pla2g4d" "Fetub"  
[17] "Kif11"   "Ccl6"    "Has3"    "Il19"    "A4galt"  "Otud1"   "Msn"     "Nqo1"   
[25] "Dbf4"    "Cblb"    "Tbc1d24" "Elmo2"   "Cd163"   "Esd"     "Rfx2"    "Gsta1"  
[33] "Slurp1"  "Arntl2"  "Vldlr"   "Tmem173" "Gpx2"    "Slfn9"   "Adh7"    "Sprr2i" 
[41] "Bcl2l15"

Plot of DESeq-normalizedcounts of genes significant in both comparisons at Week 2:

# Get the DESeq-normalize counts
dp1 <- list()
for (i in 1:length(lgene.w2)) {
  out <- plotCounts(dds, 
                    gene = lgene.w2[[i]],
                    intgroup = c("trt",
                                 "time"),
                    returnData = TRUE)
  dp1[[i]] <- data.table(Geneid = lgene.w2[[i]],
                         Sample = rownames(out),
                         out)
}
dp1 <- rbindlist(dp1)
dp1$trt <- factor(dp1$trt,
                  levels = c("CON",
                             "UVB",
                             "SFN"))
dp1$time <- factor(dp1$time,
                   levels = c("02w",
                              "15w",
                              "25w"),
                   labels = c("Week 2",
                              "Week 15",
                              "Week 25"))
dp1$Geneid <- factor(dp1$Geneid,
                     levels = lgene.w2)
dp1[, mu := mean(count,
                 na.rm = TRUE),
    by = c("Geneid",
           "trt",
           "time")]
dmu <- unique(dp1[, -c("Sample",
                       "count")])
datatable(head(dmu),
          rownames = FALSE,
          class = "cell-border stripe") %>% 
  formatRound(columns = 4,
              digits = 2)
List of 1
 $ axis.text.x:List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : num 1
  ..$ vjust        : NULL
  ..$ angle        : num 45
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi FALSE
 - attr(*, "validate")= logi TRUE

Did down-up-down trend persist?

dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$up.dn]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$up.dn]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)

List of 1
 $ axis.text.x:List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : num 1
  ..$ vjust        : NULL
  ..$ angle        : num 45
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi FALSE
 - attr(*, "validate")= logi TRUE

Did up-down-up trend persist?

dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$dn.up]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$dn.up]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)

In many of these genes, UVB+SFN moved closer to UVB over time.

Heatmap for Week 2 differentially methylated genes

up.dn.w2 <- unique(as.character(dmu.w2$Geneid[dmu.w2$up.dn]))
dn.up.w2 <- unique(as.character(dmu.w2$Geneid[dmu.w2$dn.up]))
ll <- unique(c(up.dn.w2,
               dn.up.w2))
# 36 genes

con_uvb_week2 <- data.table(Geneid = res_con_uvb_week2@rownames,
                            log2FoldChange = res_con_uvb_week2@listData$log2FoldChange)

sfn_uvb_week2 <- data.table(Geneid = res_sfn_uvb_week2@rownames,
                            log2FoldChange = -res_sfn_uvb_week2@listData$log2FoldChange)

t1 <- merge(con_uvb_week2[con_uvb_week2$Geneid %in% ll, ],
            sfn_uvb_week2[sfn_uvb_week2$Geneid %in% ll, ],
            by = "Geneid")
colnames(t1)[2:3] <- c("Control vs. UVB",
                       "UVB vs. SFN+UVB")
t1 <- t1[order(t1$`Control vs. UVB`,
               decreasing = TRUE), ]
write.csv(t1,
          file = "tmp/w2_sign_changes.csv",
          row.names = FALSE)

ll <- melt.data.table(data = t1,
                      id.vars = 1,
                      measure.vars = 2:3,
                      variable.name = "Comparison",
                      value.name = "Gene Expression Diff")
ll$Comparison <- factor(ll$Comparison,
                        levels = c("Control vs. UVB",
                                   "UVB vs. SFN+UVB"))
lvls <- ll[ll$Comparison == "Control vs. UVB", ]
ll$Geneid <- factor(ll$Geneid,
                    levels = lvls$Geneid[order(lvls$`Gene Expression Diff`)])

# Add dendrogram----
dt.dndr <- data.frame(t1[Geneid %in% levels(ll$Geneid), ])
rownames(dt.dndr) <- dt.dndr$Gene
dt.dndr <- dt.dndr[, -1]

# Compute distances between genes----
sampleDists <- dist(dt.dndr)

# Make dendrogram data----
dhc <- as.dendrogram(hclust(d = sampleDists),
                     horiz = TRUE)
ddata <- dendro_data(dhc, 
                     type = "rectangle")

# Segment data----
dtp1 <- segment(ddata)

# Hitmap data----
dtp2 <- ll
dtp2$Geneid <- factor(dtp2$Geneid,
                      levels = ddata$labels$label)

offset.size <- 4

p1 <- ggplot(data = dtp2) +
  coord_polar("y",
              start = -0.3,
              direction = -1) +
  geom_tile(aes(x =  as.numeric(Comparison),
                y = Geneid, 
                fill = `Gene Expression Diff`),
            color = "white") +
  geom_text(data = dtp2[Comparison == "Control vs. UVB", ],
            aes(x = rep(1.75,
                        nlevels(Geneid)),
                y = Geneid,
                angle = 90 + seq(from = 30,
                                 to = 330,
                                 length.out = nlevels(Geneid))[as.numeric(Geneid)] + 
                  offset.size,
                label = unique(Geneid)),
            hjust = 0) +
  geom_text(data = dtp2[Geneid == levels(dtp2$Geneid)[1], ],
            aes(x = 1:nlevels(Comparison),
                y = rep(-offset.size,
                        nlevels(Comparison)),
                angle = 0,
                label = levels(Comparison)),
            hjust = 1,
            size = 5) +
  scale_fill_gradient2(low = "red", 
                       high = "green", 
                       mid = "grey", 
                       midpoint = 0, 
                       name = "") +
  scale_y_discrete("",
                   expand = c(0, 0)) +
  theme(plot.title = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.background = element_blank(),
        legend.position = "bottom",
        legend.text = element_text(size = 15),
        legend.direction = "horizontal",
        legend.key.width = unit(1, "in"),
        legend.key.height = unit(0.3, "in")) +
  geom_segment(data = dtp1,
               aes(x = -sqrt(y) + 0.5,
                   y = x, 
                   xend = -sqrt(yend) + 0.5,
                   yend = xend),
               size = 1) 

tiff(filename = "tmp/skin_ubv_w2_sfn_hitmap_with_phylo.tiff",
     height = 8,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)

Venn Diagram, Week 2

# 1. Ctrl vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 1546, 9%
# LFC < 0 (down)     : 1537, 8.9%
# 23 genes down-up-down

# 2. SFN+UVB vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 26, 0.15%
# LFC < 0 (down)     : 35, 0.2%
# 13 gens up-down-up

p1 <- ggplot() +
  geom_circle(aes(x0 = c(1, 2, 1, 2),
                  y0 = c(1, 1, 4, 4),
                  r = rep(1, 4),
                  color = factor(c(2, 1, 1, 2))),
              size = 2) +
  geom_text(aes(x = rep(c(0.5, 1.5, 2.5), 2),
                y = rep(c(1, 4), each = 3),
                label = format(c(26, 13, 35, 1546, 23, 1537),
                               big.mark = ","))) +
  scale_color_manual(values = c("green", "red")) +
  theme_void() +
  theme(legend.position = "none")

tiff(filename = "tmp/skin_ubv_sfn_w2_venn.tiff",
     height = 6,
     width = 4,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)

Effect of UVB at Week 15

res_con_uvb_week15 <- results(dds,
                              contrast = c(0,0,0,1,0,1,0,0,0),
                              alpha = 0.1)
res_con_uvb_week15 <- res_con_uvb_week15[order(res_con_uvb_week15$padj,
                                               decreasing = FALSE),]
summary(res_con_uvb_week15)

out of 17202 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 1513, 8.8%
LFC < 0 (down)     : 1463, 8.5%
outliers [1]       : 0, 0%
low counts [2]     : 2668, 16%
(mean count < 2)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 1513, 8.8%
# LFC < 0 (down)     : 1463, 8.5%
# outliers [1]       : 0, 0%
# low counts [2]     : 2668, 16%
# (mean count < 2)

# How many adjusted p-values were less than 0.01?
sum(res_con_uvb_week15$padj < 0.1, 
    na.rm = TRUE)
[1] 2976
# 2976

# NOT THE SAME AS?!!!:
res_con_uvb_week15.1 <- results(dds,
                                contrast = list("trt_CON_vs_UVB",
                                                "time15w.trtCON"),
                                alpha = 0.1)
res_con_uvb_week15.1 <- res_con_uvb_week15.1[order(res_con_uvb_week15.1$padj,
                                                   decreasing = FALSE),]
summary(res_con_uvb_week15.1)

out of 17202 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 469, 2.7%
LFC < 0 (down)     : 455, 2.6%
outliers [1]       : 0, 0%
low counts [2]     : 4002, 23%
(mean count < 6)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 469, 2.7%
# LFC < 0 (down)     : 455, 2.6%
# outliers [1]       : 0, 0%
# low counts [2]     : 4002, 23%
# (mean count < 6)

# How many adjusted p-values were less than 0.01?
sum(res_con_uvb_week15.1$padj < 0.1, 
    na.rm = TRUE)
[1] 924
# 924

# MA plot
# Save for publication
tiff(filename = "tmp/ma_w15_con_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plotMA(res_con_uvb_week15,
       main = "Control vs. UVB at Week 15",
       alpha = 0.8)
graphics.off()

plotMA(res_con_uvb_week15,
       main = "Control vs. UVB at Week 15",
       alpha = 0.8)

Protective effect of SFN at Week 15

res_sfn_uvb_week15 <- results(dds,
                              contrast = c(0,0,0,0,1,0,0,1,0),
                              alpha = 0.1)
res_sfn_uvb_week15 <- res_sfn_uvb_week15[order(res_sfn_uvb_week15$padj,
                                               decreasing = FALSE),]
summary(res_sfn_uvb_week15)

out of 17202 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 20, 0.12%
LFC < 0 (down)     : 10, 0.058%
outliers [1]       : 0, 0%
low counts [2]     : 7004, 41%
(mean count < 53)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 20, 0.12%
# LFC < 0 (down)     : 10, 0.058%
# outliers [1]       : 0, 0%
# low counts [2]     : 7004, 41%
# (mean count < 53)

# How many adjusted p-values were less than 0.05?
sum(res_sfn_uvb_week15$padj < 0.1, 
    na.rm = TRUE)
[1] 30
# 30

# NOT THE SAME AS!!!:
res_sfn_uvb_week15.1 <- results(dds,
                                contrast = list("trt_SFN_vs_UVB",
                                                "time15w.trtSFN"),
                                alpha = 0.1)
res_sfn_uvb_week15.1 <- res_sfn_uvb_week15.1[order(res_sfn_uvb_week15.1$padj,
                                                   decreasing = FALSE),]
summary(res_sfn_uvb_week15.1)

out of 17202 with nonzero total read count
adjusted p-value < 0.1
LFC > 0 (up)       : 14, 0.081%
LFC < 0 (down)     : 24, 0.14%
outliers [1]       : 0, 0%
low counts [2]     : 3335, 19%
(mean count < 4)
[1] see 'cooksCutoff' argument of ?results
[2] see 'independentFiltering' argument of ?results
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 14, 0.081%
# LFC < 0 (down)     : 24, 0.14%
# outliers [1]       : 0, 0%
# low counts [2]     : 3335, 19%
# (mean count < 4)

# How many adjusted p-values were less than 0.05?
sum(res_sfn_uvb_week15.1$padj < 0.1, 
    na.rm = TRUE)
[1] 38
# 38

# MA plot
# Save for publication
tiff(filename = "tmp/ma_w2_sfn_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(plotMA(res_sfn_uvb_week15,
             main = "UVB+SFN vs UVB at Week 15",
             alpha = 0.8))
NULL
graphics.off()

print(plotMA(res_sfn_uvb_week15,
             main = "UVB+SFN vs UVB at Week 15",
             alpha = 0.8))
NULL

Genes that were significantly differentiated at both comparisons at Week 15

lgene.w15.con <- unique(res_con_uvb_week15@rownames[res_con_uvb_week15$padj < 0.1])
lgene.w15.sfn <- unique(res_sfn_uvb_week15@rownames[res_sfn_uvb_week15$padj < 0.1])
lgene.w15 <- lgene.w15.con[lgene.w15.con %in% lgene.w15.sfn]
lgene.w15 <- lgene.w15 [!is.na(lgene.w15 )]
length(unique(lgene.w15))
[1] 15

Plot of DESeq-normalizedcounts of genes significant in both comparisons at Week 15:

# Get the DESeq-normalize counts
dp1 <- list()
for (i in 1:length(lgene.w15)) {
  out <- plotCounts(dds, 
                    gene = lgene.w15[[i]],
                    intgroup = c("trt",
                                 "time"),
                    returnData = TRUE)
  dp1[[i]] <- data.table(Geneid = lgene.w15[[i]],
                         Sample = rownames(out),
                         out)
}
dp1 <- rbindlist(dp1)
dp1$trt <- factor(dp1$trt,
                  levels = c("CON",
                             "UVB",
                             "SFN"))
dp1$time <- factor(dp1$time,
                   levels = c("02w",
                              "15w",
                              "25w"),
                   labels = c("Week 2",
                              "Week 15",
                              "Week 25"))
dp1$Geneid <- factor(dp1$Geneid,
                     levels = lgene.w15)
dp1[, mu := mean(count,
                 na.rm = TRUE),
    by = c("Geneid",
           "trt",
           "time")]
dmu <- unique(dp1[, -c("Sample",
                       "count")])
datatable(head(dmu),
          rownames = FALSE,
          class = "cell-border stripe") %>% 
  formatRound(columns = 4,
              digits = 2)
List of 1
 $ axis.text.x:List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : num 1
  ..$ vjust        : NULL
  ..$ angle        : num 45
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi FALSE
 - attr(*, "validate")= logi TRUE

Did down-up-down trend persist?

dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$up.dn]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$up.dn]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)

List of 1
 $ axis.text.x:List of 11
  ..$ family       : NULL
  ..$ face         : NULL
  ..$ colour       : NULL
  ..$ size         : NULL
  ..$ hjust        : num 1
  ..$ vjust        : NULL
  ..$ angle        : num 45
  ..$ lineheight   : NULL
  ..$ margin       : NULL
  ..$ debug        : NULL
  ..$ inherit.blank: logi FALSE
  ..- attr(*, "class")= chr [1:2] "element_text" "element"
 - attr(*, "class")= chr [1:2] "theme" "gg"
 - attr(*, "complete")= logi FALSE
 - attr(*, "validate")= logi TRUE

Did up-down-up trend persist?

dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$dn.up]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$dn.up]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)

Heatmap for Week 15 differentially methylated genes

up.dn.w15 <- unique(as.character(dmu.w15$Geneid[dmu.w15$up.dn]))
dn.up.w15 <- unique(as.character(dmu.w15$Geneid[dmu.w15$dn.up]))
ll <- unique(c(up.dn.w15,
               dn.up.w15))
# 16 genes

con_uvb_week15 <- data.table(Geneid = res_con_uvb_week15@rownames,
                             log2FoldChange = res_con_uvb_week15@listData$log2FoldChange)

sfn_uvb_week15 <- data.table(Geneid = res_sfn_uvb_week15@rownames,
                             log2FoldChange = -res_sfn_uvb_week15@listData$log2FoldChange)

t1 <- merge(con_uvb_week15[con_uvb_week15$Geneid %in% ll, ],
            sfn_uvb_week15[sfn_uvb_week15$Geneid %in% ll, ],
            by = "Geneid")
colnames(t1)[2:3] <- c("Control vs. UVB",
                       "UVB vs. SFN+UVB")
t1 <- t1[order(t1$`Control vs. UVB`,
               decreasing = TRUE), ]
write.csv(t1,
          file = "tmp/w15_sign_changes.csv",
          row.names = FALSE)

ll <- melt.data.table(data = t1,
                      id.vars = 1,
                      measure.vars = 2:3,
                      variable.name = "Comparison",
                      value.name = "Gene Expression Diff")
ll$Comparison <- factor(ll$Comparison,
                        levels = c("Control vs. UVB",
                                   "UVB vs. SFN+UVB"))
lvls <- ll[ll$Comparison == "Control vs. UVB", ]
ll$Geneid <- factor(ll$Geneid,
                    levels = lvls$Geneid[order(lvls$`Gene Expression Diff`)])

# Add dendrogram----
dt.dndr <- data.frame(t1[Geneid %in% levels(ll$Geneid), ])
rownames(dt.dndr) <- dt.dndr$Gene
dt.dndr <- dt.dndr[, -1]

# Compute distances between genes----
sampleDists <- dist(dt.dndr)

# Make dendrogram data----
dhc <- as.dendrogram(hclust(d = sampleDists),
                     horiz = TRUE)
ddata <- dendro_data(dhc, 
                     type = "rectangle")

# Segment data----
dtp1 <- segment(ddata)

# Hitmap data----
dtp2 <- ll
dtp2$Geneid <- factor(dtp2$Geneid,
                      levels = ddata$labels$label)

offset.size <- 4

p1 <- ggplot(data = dtp2) +
  coord_polar("y",
              start = -0.3,
              direction = -1) +
  geom_tile(aes(x =  as.numeric(Comparison),
                y = Geneid, 
                fill = `Gene Expression Diff`),
            color = "white") +
  geom_text(data = dtp2[Comparison == "Control vs. UVB", ],
            aes(x = rep(1.75,
                        nlevels(Geneid)),
                y = Geneid,
                angle = 90 + seq(from = 90,
                                 to = 330,
                                 length.out = nlevels(Geneid))[as.numeric(Geneid)] + 
                  offset.size,
                label = unique(Geneid)),
            hjust = 0) +
  geom_text(data = dtp2[Geneid == levels(dtp2$Geneid)[1], ],
            aes(x = 1:nlevels(Comparison),
                y = rep(-offset.size,
                        nlevels(Comparison)),
                angle = 0,
                label = levels(Comparison)),
            hjust = 1,
            size = 5) +
  scale_fill_gradient2(low = "red", 
                       high = "green", 
                       mid = "grey", 
                       midpoint = 0, 
                       name = "") +
  scale_y_discrete("",
                   expand = c(0, 0)) +
  theme(plot.title = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.background = element_blank(),
        legend.position = "bottom",
        legend.text = element_text(size = 15),
        legend.direction = "horizontal",
        legend.key.width = unit(1, "in"),
        legend.key.height = unit(0.3, "in")) +
  geom_segment(data = dtp1,
               aes(x = -sqrt(y) + 0.5,
                   y = x, 
                   xend = -sqrt(yend) + 0.5,
                   yend = xend),
               size = 1) 

tiff(filename = "tmp/skin_ubv_w15_sfn_hitmap_with_phylo.tiff",
     height = 8,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)

Venn Diagram, Week 15

# out of 17202 with nonzero total read count
# 1. Ctrl vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 1513, 8.8%
# LFC < 0 (down)     : 1463, 8.5%
# 2 genes down-up-down

# 2. SFN+UVB vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 20, 0.12%
# LFC < 0 (down)     : 10, 0.058%
# 9 gens up-down-up

p1 <- ggplot() +
  geom_circle(aes(x0 = c(1, 2, 1, 2),
                  y0 = c(1, 1, 4, 4),
                  r = rep(1, 4),
                  color = factor(c(2, 1, 1, 2))),
              size = 2) +
  geom_text(aes(x = rep(c(0.5, 1.5, 2.5), 2),
                y = rep(c(1, 4), each = 3),
                label = format(c(20, 9, 10, 1513, 2, 1463),
                               big.mark = ","))) +
  scale_color_manual(values = c("green", "red")) +
  theme_void() +
  theme(legend.position = "none")

tiff(filename = "tmp/skin_ubv_sfn_w2_venn.tiff",
     height = 6,
     width = 4,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)

Interactions terms

Tests if the effect of NOT treating with UVB vs. treating with UVB is different at Week 15 compared to Week 2:

res_int_con_uvb_week <- results(dds, 
                                name = "time15w.trtCON",
                                alpha = 0.1)
res_int_con_uvb_week <- res_int_con_uvb_week[order(res_int_con_uvb_week$padj,
                                                   decreasing = FALSE),]
print(res_int_con_uvb_week)
summary(res_int_con_uvb_week)

# How many adjusted p-values were less than 0.05?
sum(res_int_con_uvb_week$padj < 0.1, 
    na.rm = TRUE)

# MA plot
print(plotMA(res_int_con_uvb_week,
             main = "(Control vs. UVB) x TIme Interaction",
             alpha = 0.9))

Tests if the effect of treating with UVB+SFN vs. treating with UVB is different at Week 15 compared to Week 2:

res_int_sfn_uvb_week <- results(dds, 
                                name = "time15w.trtSFN",
                                alpha = 0.1)
res_int_sfn_uvb_week <- res_int_sfn_uvb_week[order(res_int_sfn_uvb_week$padj,
                                                   decreasing = FALSE),]
print(res_int_sfn_uvb_week)
summary(res_int_sfn_uvb_week)

# How many adjusted p-values were less than 0.05?
sum(res_int_sfn_uvb_week$padj < 0.1, 
    na.rm = TRUE)

# MA plot
print(plotMA(res_int_sfn_uvb_week))

# NOTE: same as 
# res <- results(dds, 
#                   alpha = 0.05)
# res <- res[order(res$padj, decreasing = FALSE),]
# res

NOTE: By default, the results(dds)* prints the results for the last level of the last term, i.e. here it was for for the interaction term SFN vs. UVB at Week 15 vs. Week 2.

Genes with both interactions being significant

lgene.con <- unique(res_int_con_uvb_week@rownames[res_int_con_uvb_week$padj < 0.1])
lgene.sfn <- unique(res_int_sfn_uvb_week@rownames[res_int_sfn_uvb_week$padj < 0.1])
lgene <- lgene.con[lgene.con %in% lgene.sfn]
lgene <- lgene[!is.na(lgene)]
lgene

Plot of DESeq-normalizedcounts of genes with smallest adjusted p-value for the interaction term:

# Get the DESeq-normalize counts
dp1 <- list()
for (i in 1:length(lgene)) {
  out <- plotCounts(dds, 
                    gene = lgene[[i]],
                    intgroup = c("trt",
                                 "time"),
                    returnData = TRUE)
  dp1[[i]] <- data.table(Geneid = lgene[[i]],
                         Sample = rownames(out),
                         out)
}
dp1 <- rbindlist(dp1)
dp1$trt <- factor(dp1$trt,
                  levels = c("CON",
                             "UVB",
                             "SFN"))
dp1$time <- factor(dp1$time,
                   levels = c("02w",
                              "15w"),
                   labels = c("Week 2",
                              "Week 15"))
dp1$Geneid <- factor(dp1$Geneid,
                     levels = lgene)
dp1[, mu := mean(count,
                 na.rm = TRUE),
    by = c("Geneid",
           "trt",
           "time")]
dmu <- unique(dp1[, -c("Sample",
                       "count")])

p1 <- ggplot(dp1,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)

Compare to the plot of TPM-normalizedcounts of genes with smallest adjusted p-value for the interaction term:

# Examine TPM values for the same genes
tmp <- tpm[Geneid %in% lgene, ]
tmp$Geneid <- factor(tmp$Geneid,
                     levels = lgene)
tmp <- melt.data.table(data = tmp,
                       id.vars = 1,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")
tmp <- merge(dmeta,
             tmp,
             by = "Sample")

p1 <- ggplot(tmp,
             aes(x = Week,
                 y = TPM,
                 fill = Treatment,
                 group = Treatment)) +
  facet_wrap(~ Geneid,
             scales = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black")+
  scale_x_discrete("")
plot(p1)

Session Information

sessionInfo()
---
title: "Skin UVB SKH1 mouse model treated with SFN "
output:
  html_notebook:
    toc: yes
    toc_float: yes
    code_folding: hide
---

# Part 1: RNA
```{r header, echo = FALSE, message = FALSE, error = FALSE, warning  =FALSE}
# if (!requireNamespace("BiocManager", quietly = TRUE))
#     install.packages("BiocManager")
# BiocManager::install("DESeq2")

require(knitr)
require(data.table)
require(DT)
require(DESeq2)
require(readxl)
require(BiocParallel)
require(ggplot2)
require(plotly)
require(threejs)
require(scales)
require(gridExtra)
require(ggpubr)
require(ggdendro)
require(ggforce)

# NOTE: on DESeq2 Output: 'baseMean' is the average of the normalized count values, 
# divided by the size factors, taken over all samples in the DESeqDataSet
```

## Load RNA samples
Out of 30 samples, we selected 17 for this study. These are the normal tissue samples form the control, the UVA and the UVA+SFN treatment groups. normal tissue samples from the UVB_UA groups as well as tumor samples were excluded from this analysis. Additionally, one of the control samples at Week 2 (baseline) was removed after outlier analysis.    
7,219 genes with zero counts in > 80% (> 13 out of 18) of samples were removed. 17,202 out of 24,421 genes were left. 
         
```{r data_rna, warning = FALSE, echo = FALSE, message = FALSE}
# Load data----
dt0 <- fread("data/renyi_dedup_rnaseq_data/featurescounts_uvb-skin_dedup_renyi_2-9-2018.csv",
             skip = 1)

# Remove unused columns----
dt1 <- dt0[, c(1, 6:ncol(dt0)), with = FALSE]

cnames <- colnames(dt1)[-c(1:2)]
cnames <- gsub(x = cnames,
               pattern = ".dedup.bam",
               replacement = "")
colnames(dt1)[-c(1:2)] <- cnames

# ATTENTION! In this analysis, we will only examine controls and SFN
# Also, removed cancer cell samples
tnames <- substr(x = colnames(dt1), 
                 start = 3,
                 stop = 3)

gnames <- substr(x = colnames(dt1), 
                 start = 5,
                 stop = 7)

dt1 <- dt1[, gnames %in% c("id",
                           "th",
                           "CON",
                           "UVB",
                           "SFN" ) &
             tnames != "t",
           with = FALSE]
# 18 samples left

# Remove sample '02w_CON_1' as an outlier
# See 'skin_uvb_sfn_exclude_con2w1_v1' for details
dt1 <- dt1[, colnames(dt1) != "02w_CON_1", with = FALSE]

# Remove genes with zero counts in > 80% (> 13 out of 17) of samples
tmp <- dt1[, -c(1:2)] == 0
tmp <- rowSums(tmp) > 13
sum(tmp)

dt1 <- droplevels(dt1[!tmp, ])
nrow(dt1)
# 17,202 out of 24,421 genes left

datatable(head(dt1, 10),
          rownames = FALSE,
          class = "cell-border stripe",
          options = list(pageLength = 10),
          caption = "Table 1: first 10 rows of the count table")
```

## Transcripts per kilobase million (TPM) normalization
Next, we noramized the counts. To convert number of hits to  the relative abundane of genes in each sample, we used ***transcripts per kilobase million (TPM)*** normalization, which is as following for the j-th sample:       
1. normilize for gene length: a[i, j] = 1,000*count[i, j]/gene[i, j] length(bp)     
2. normalize for seq depth (i.e. total count): a(i, j)/sum(a[, j])     
3. multiply by one million     
A very good comparison of normalization techniques can be found at the following video:    
[RPKM, FPKM and TPM, clearly explained](https://www.rna-seqblog.com/rpkm-fpkm-and-tpm-clearly-explained/)
     
After the normalization, each sample's total is 1M:
     
```{r tpm, warning = FALSE, echo = FALSE, message = FALSE}
# Normalize counts to TPM
tmp <- 1000*dt1[, 3:ncol(dt1)]/dt1$Length
tpm <- data.table(Geneid = dt1$Geneid,
                  Length = dt1$Length,
                  apply(tmp,
                        2,
                        function(a) {
                          10^6*(a/sum(a))
                        }))
colSums(tpm[, -c(1:2)])

datatable(head(tpm, 10),
          rownames = FALSE,
          class = "cell-border stripe",
          options = list(pageLength = 10),
          caption = "Table 2: transcripts per kilobase million (TPM) normalized counts") %>% 
  formatRound(columns = 3:ncol(tpm),
              digits = 2) %>%
  formatStyle(columns = 3:ncol(tpm),
              color = "black",
              backgroundColor = styleInterval(cuts = c(10, 100),
                                              values = c("white",
                                                         "yellow",
                                                         "red")))
# Total TPM
total <- rowSums(tpm[, 3:ncol(tpm)])

# Sort genes by relative abundancy
tpm$Geneid <- factor(tpm$Geneid ,
                     levels = tpm$Geneid[order(total,
                                               decreasing = FALSE)])
```

Color Legend:    
**YELLOW**: TMP > 10      
**RED**: TMP > 100    

# Top 100 most abundant RNA molecules
```{r most_abundant}
# Separate top 100 abundant genes
tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[(nrow(tpm) - 99):nrow(tpm)]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p2 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p2)
```

# Bottom 100 least abundant RNA molecules
```{r least_abundant}
tmp <- droplevels(tpm[Geneid %in% levels(tpm$Geneid)[1:100]])

tmp <- melt.data.table(data = tmp,
                       id.vars = 1:2,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")

tmp$Week <- substr(x = tmp$Sample,
                   start = 1,
                   stop = 3)
tmp$Week <- factor(tmp$Week,
                   levels = unique(tmp$Week))


tmp$Treatment <- substr(x = tmp$Sample,
                        start = 5,
                        stop = 7)
tmp$Treatment <- factor(tmp$Treatment,
                        levels = c("CON", 
                                   "UVB",
                                   "SFN"))

tmp$Replica <- substr(x = tmp$Sample,
                      start = 9,
                      stop = 9)
tmp$Replica <- factor(tmp$Replica,
                      levels = 0:1)

# Plot top 100 abundant genes
p3 <- ggplot(tmp,
             aes(x = TPM,
                 y = Geneid,
                 fill = Treatment,
                 shape = Week)) +
  # facet_wrap(~ Sex, nrow = 1) +
  geom_point(size = 3,
             alpha = 0.5) +
  geom_vline(xintercept = 1,
             linetype = "dashed")
ggplotly(p3)
```

# Meta data
```{r meta}
dmeta <- data.table(Sample = colnames(dt1)[-c(1:2)])

dmeta$time <- substr(x = dmeta$Sample,
                     start = 1,
                     stop = 3)
dmeta$time <- factor(dmeta$time,
                     levels = c("02w",
                                "15w",
                                "25w"))
dmeta$Week <- factor(dmeta$time,
                     levels = c("02w",
                                "15w",
                                "25w"),
                     labels = c("Week 2",
                                "Week 15",
                                "Week 25"))

dmeta$trt <- substr(x = dmeta$Sample,
                    start = 5,
                    stop = 7)
dmeta$trt <- factor(dmeta$trt,
                    levels = c("CON", 
                               "UVB",
                               "SFN"))
dmeta$Treatment <- factor(dmeta$trt,
                          levels = c("CON", 
                                     "UVB",
                                     "SFN"),
                          labels = c("Negative Control",
                                     "Positive Control (UVB)",
                                     "Sulforaphane (SFN)"))

dmeta$Replica <- substr(x = dmeta$Sample,
                        start = 9,
                        stop = 9)
dmeta$Replica <- factor(dmeta$Replica,
                        levels = 0:1)

datatable(dmeta,
          rownames = FALSE,
          class = "cell-border stripe",
          options = list(pageLength = nrow(dmeta)))
```

# PCA of TPM
NOTE: the distributions are skewed. To make them symmetric, log transformation is often applied. However, there is an issue of zeros. In this instance, we added a small values ***lambda[i]*** equal to 1/10 of the smallest non-zero value of *i*-th gene. 
```{r pca}
dm.tpm <- as.matrix(tpm[, -c(1:2), with = FALSE])
rownames(dm.tpm) <- tpm$Geneid

# # Remove 02w_CON_1 sample and redo PCA
# dm.tpm <- dm.tpm[, colnames(dm.tpm) != "02w_CON_1"]
# dmeta <- dmeta[dmeta$Sample != "02w_CON_1", ]

# Add lambdas to all values, then take a log
dm.ltpm <- t(apply(X = dm.tpm,
                      MARGIN = 1,
                      FUN = function(a) {
                        lambda <- min(a[a > 0])/10
                        log(a + lambda)
                      }))

# PCA----
m1 <- prcomp(t(dm.ltpm),
             center = TRUE,
             scale. = TRUE)

s1 <- summary(m1)
s1
```

# Pareto chart of variance explained by principal components
```{r pca_var_plot}
imp <- data.table(PC = colnames(s1$importance),
                  Variance = 100*s1$importance[2, ],
                  Cumulative = 100*s1$importance[3, ])
imp$PC <- factor(imp$PC,
                 levels = imp$PC)
p1 <- ggplot(imp,
             aes(x = PC,
                 y = Variance)) +
  geom_bar(stat = "identity",
           fill = "grey",
           color = "black") +
  geom_line(aes(y = rescale(Cumulative,
                            to = c(min(Cumulative)*max(imp$Variance)/100,
                                   max(imp$Variance))),
                group = rep(1, nrow(imp)))) +
  geom_point(aes(y = rescale(Cumulative,
                             to = c(min(Cumulative)*max(imp$Variance)/100,
                                    max(imp$Variance))))) +
  scale_y_continuous("% Variance Explained",
                     breaks = seq(from = 0, 
                                  to = max(imp$Variance), 
                                  by = 5),
                     labels = paste(seq(from = 0, 
                                        to = max(imp$Variance),
                                        by = 5),
                                    "%",
                                    sep = ""),
                     sec.axis = sec_axis(trans = ~.,
                                         name = "% Cumulative Variance",
                                         breaks = seq(from = 0, 
                                                      to = max(imp$Variance), 
                                                      length.out = 5),
                                         labels = paste(seq(from = 0, 
                                                            to = 100, 
                                                            length.out = 5),
                                                        "%",
                                                        sep = ""))) +
  scale_x_discrete("") +
  theme(axis.text.x = element_text(angle = 90,
                                   hjust = 1))

# Save for publication
tiff(filename = "tmp/pca_pareto.tiff",
     height = 6,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(p1)
graphics.off()

print(p1)
```

# First 3 principal components, pairwise
```{r pca_plots}
# Biplot while keep only the most important variables (Javier)----
# Select PC-s to pliot (PC1 & PC2)
choices <- c(1:3)

# Scores, i.e. points (df.u)
dt.scr <- data.table(m1$x[, choices])
# Add grouping variables
dt.scr$trt <- dmeta$trt
dt.scr$time <- dmeta$time
dt.scr$sample <- dmeta$Sample

# Loadings, i.e. arrows (df.v)
dt.rot <- as.data.frame(m1$rotation[, choices])
dt.rot$feat <- rownames(dt.rot)
dt.rot <- data.table(dt.rot)

# Axis labels
u.axis.labs <- paste(colnames(dt.rot)[choices], 
                     sprintf('(%0.1f%% explained var.)', 
                             100*m1$sdev[choices]^2/sum(m1$sdev^2)))

p1 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC2,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[2]) +
  theme(legend.position = "none")
ggplotly(p1)

p2 <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[1]) +
  scale_y_continuous(u.axis.labs[3]) +
  theme(legend.position = "none")
ggplotly(p2)

p3 <- ggplot(data = dt.scr,
             aes(x = PC2,
                 y = PC3,
                 color = trt,
                 shape = time)) +
  geom_point(size = 4,
             alpha = 0.5) +
  scale_x_continuous(u.axis.labs[2]) +
  scale_y_continuous(u.axis.labs[3]) +
  theme(legend.position = "none")
ggplotly(p3)

# Legend only
tmp <- ggplot(data = dt.scr,
             aes(x = PC1,
                 y = PC2,
                 color = trt,
                 shape = time)) +
  geom_point() +
  scale_color_discrete("Treatment") +
  scale_shape_discrete("Week")
p4 <- as_ggplot(get_legend(tmp))

# Save for publication
tiff(filename = "tmp/pca.tiff",
     height = 7,
     width = 9,
     units = 'in',
     res = 600,
     compression = "lzw+p")
grid.arrange(p1, p2, p3, p4, 
             nrow = 2)
graphics.off()
```

# First 3 principal components, 3D
```{r pca_3d, fig.height = 10, fig.width = 10}
scatterplot3js(x = dt.scr$PC1, 
               y = dt.scr$PC2, 
               z = dt.scr$PC3, 
               color = as.numeric(dt.scr$trt),
               renderer = "auto",
               pch = dt.scr$sample,
               size = 0.1)
```

# Differential expression analysis (DESeq2 pipeline)
Sources:    
1. [Analyzing RNA-seq data with DESeq2:Interactions](https://www.bioconductor.org/packages/devel/bioc/vignettes/DESeq2/inst/doc/DESeq2.html#interactions)     
2. [Bioconductor Question: DESeq2 time series analysis](https://support.bioconductor.org/p/97430/)      
We are testing a model with time*treatment interaction. The idea here is to find genes with significant interaction term. That would suggest that the gene expressiondifferences between the treatments depended on time. THere are several possible scenarios:    
a. No difference between the negative control and the positive control groups at baseline, significant difference at the later time point. This will show the effect of the disease (UVB radiation, in this case).     
b. Significant difference between the control groups at baseline, no difference at the later time point. Same as (a) above.     
c. Differences between the positive control and the SFN-treated groups. Here, we are interested in the reversal of UVB effect. Again, the interaction term will need to be significant for the reasons described above.      

```{r deseq2}
# Relevel: make all comparisons with the positive control (UVB)
dmeta$trt <- factor(dmeta$trt,
                    levels = c("UVB",
                               "CON",
                               "SFN"))

dtm<- as.matrix(dt1[, dmeta$Sample,
                    with = FALSE])
rownames(dtm) <- dt1$Geneid

dds <- DESeqDataSetFromMatrix(countData = dtm, 
                              colData = dmeta,
                              ~ time + trt + time:trt)
# If all samples contain zeros, geometric means cannot be
# estimated. Change default 'type = "ratio"' to 'type = "poscounts"'.
# Type '?DESeq2::estimateSizeFactors' for more details.
dds <- estimateSizeFactors(object = dds,
                           type = "poscounts")

# Run DESeq----
dds <- DESeq(object = dds,
             # test = "LRT",
             # reduced = ~ time + trt,
             fitType = "local",
             sfType = "ratio",
             parallel = FALSE)

# NOTE (from DESeq help file, section Value):
# A DESeqDataSet object with results stored as metadata columns. 
# These results should accessed by calling the results function. 
# By default this will return the log2 fold changes and p-values
# for the last variable in the design formula. 
# See results for how to access results for other variables.
# In this case, the last term is the interaction term trt:time

# NOTE: 
# Likelihood ratio test (LRT) (chi-squared test) for GLM will only return 
# the results for the difference between the full and the reduced model

resultsNames(dds)

# Model matrix
mm1 <- model.matrix(~ time + trt + time:trt, dmeta)
mm1

head(mcols(dds))
```

# Results
## Effect of UVB at Week 2
```{r deseq2_results_week2_con_uvb}
# res_con_uvb_week2 <- results(dds,
#                              contrast = c(0,0,0,1,0,0,0,0,0),
#                              alpha = 0.1)
# SAME AS:
res_con_uvb_week2 <- results(dds,
                             name = "trt_CON_vs_UVB",
                             alpha = 0.1)
res_con_uvb_week2 <- res_con_uvb_week2[order(res_con_uvb_week2$padj,
                                                   decreasing = FALSE),]
summary(res_con_uvb_week2)

# How many adjusted p-values were less than 0.05?
sum(res_con_uvb_week2$padj < 0.1, 
    na.rm = TRUE)

# MA plot
# Save for publication
tiff(filename = "tmp/ma_w2_con_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plotMA(res_con_uvb_week2,
             main = "Control vs. UVB at Week 2",
             alpha = 0.8)
graphics.off()

plotMA(res_con_uvb_week2,
             main = "Control vs. UVB at Week 2",
             alpha = 0.8)
```

## Protective effect of SFN at Week 2
```{r deseq2_results_week2_sfn_uvb}
# res_sfn_uvb_week2 <- results(dds,
#                              contrast = c(0,0,0,0,1,0,0,0,0),
#                              alpha = 0.1)
# SAME AS;
res_sfn_uvb_week2 <- results(dds,
                             name = "trt_SFN_vs_UVB",
                             alpha = 0.1)
res_sfn_uvb_week2 <- res_sfn_uvb_week2[order(res_sfn_uvb_week2$padj,
                                                   decreasing = FALSE),]
summary(res_sfn_uvb_week2)

# How many adjusted p-values were less than 0.05?
sum(res_sfn_uvb_week2$padj < 0.1, 
    na.rm = TRUE)

# MA plot
# Save for publication
tiff(filename = "tmp/ma_w2_sfn_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(plotMA(res_sfn_uvb_week2,
             main = "UVB+SFN vs UVB at Week 2",
             alpha = 0.8))
graphics.off()

print(plotMA(res_sfn_uvb_week2,
             main = "UVB+SFN vs UVB at Week 2",
             alpha = 0.8))
```

## Genes that were significantly differentiated at both comparisons at Week 2
```{r sign_w2}
lgene.w2.con <- unique(res_con_uvb_week2@rownames[res_con_uvb_week2$padj < 0.1])
lgene.w2.sfn <- unique(res_sfn_uvb_week2@rownames[res_sfn_uvb_week2$padj < 0.1])
lgene.w2 <- lgene.w2.con[lgene.w2.con %in% lgene.w2.sfn]
lgene.w2 <- lgene.w2 [!is.na(lgene.w2 )]
lgene.w2
```

Plot of DESeq-normalizedcounts of genes significant in both comparisons at Week 2:   

```{r deseq2_w2sign_deseqnorm}
# Get the DESeq-normalize counts
dp1 <- list()
for (i in 1:length(lgene.w2)) {
  out <- plotCounts(dds, 
                    gene = lgene.w2[[i]],
                    intgroup = c("trt",
                                 "time"),
                    returnData = TRUE)
  dp1[[i]] <- data.table(Geneid = lgene.w2[[i]],
                         Sample = rownames(out),
                         out)
}
dp1 <- rbindlist(dp1)
dp1$trt <- factor(dp1$trt,
                  levels = c("CON",
                             "UVB",
                             "SFN"))
dp1$time <- factor(dp1$time,
                   levels = c("02w",
                              "15w",
                              "25w"),
                   labels = c("Week 2",
                              "Week 15",
                              "Week 25"))
dp1$Geneid <- factor(dp1$Geneid,
                     levels = lgene.w2)
dp1[, mu := mean(count,
                 na.rm = TRUE),
    by = c("Geneid",
           "trt",
           "time")]
dmu <- unique(dp1[, -c("Sample",
                       "count")])
datatable(head(dmu),
          rownames = FALSE,
          class = "cell-border stripe") %>% 
  formatRound(columns = 4,
              digits = 2)
```

```{r deseq2_w2sign_deseqnorm_w2_up_dn, echo = FALSE, message = FALSE, fig.height = 6, fig.width = 8}
dmu.w2 <- dmu[time == "Week 2", ]
dmu.w2[, up.dn := (mu[trt == "UVB"] > mu[trt == "CON"]) &
               (mu[trt == "UVB"] > mu[trt == "SFN"]),
       by = Geneid]
p1 <- ggplot(dmu.w2[up.dn == TRUE, ],
             aes(x = trt,
                 y = mu,
                 group = Geneid,
                 fill = trt)) +
        facet_wrap(~ Geneid,
                   scale = "free_y") +
        geom_line(position = position_dodge(0.5)) +
        geom_point(position = position_dodge(0.5),
                   shape = 21,
                   size = 3,
                   color = "black") +
        scale_x_discrete("") +
        scale_y_continuous("Differentially Expressed Genes at Week 2") +
        scale_fill_discrete("Treatment") +
        ggtitle("Genes Upregulated by UVB")
theme(axis.text.x = element_text(angle = 45,
                                 hjust = 1))

tiff(filename = "tmp/w2_up_dn.tiff",
     height = 6,
     width = 8,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

print(p1)
```

## Did down-up-down trend persist?
```{r deseq2_w2sign_deseqnorm_plot_all_up_dn, fig.height = 10, fig.width = 12}
dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$up.dn]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$up.dn]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)
```

```{r deseq2_w2sign_deseqnorm_w2_dn_up, echo = FALSE, message = FALSE, fig.height = 6, fig.width = 8}
dmu.w2[, dn.up := (mu[trt == "UVB"] < mu[trt == "CON"]) &
               (mu[trt == "UVB"] < mu[trt == "SFN"]),
       by = Geneid]
p2 <- ggplot(dmu.w2[dn.up == TRUE, ],
             aes(x = trt,
                 y = mu,
                 group = Geneid,
                 fill = trt)) +
        facet_wrap(~ Geneid,
                   scale = "free_y") +
        geom_line(position = position_dodge(0.5)) +
        geom_point(position = position_dodge(0.5),
                   shape = 21,
                   size = 3,
                   color = "black") +
        scale_x_discrete("") +
        scale_y_continuous("Differentially Expressed Genes at Week 2") +
        scale_fill_discrete("Treatment") +
        ggtitle("Genes Downregulated by UVB")
theme(axis.text.x = element_text(angle = 45,
                                 hjust = 1))

tiff(filename = "tmp/w2_dn_up.tiff",
     height = 6,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(p2)
graphics.off()

print(p2)
```

## Did up-down-up trend persist?
```{r deseq2_w2sign_deseqnorm_plot_all_dn_up, fig.height = 10, fig.width = 12}
dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$dn.up]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w2$Geneid[dmu.w2$dn.up]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)
```

In many of these genes, UVB+SFN moved closer to UVB over time.

## Heatmap for Week 2 differentially methylated genes
```{r w2_heatmap, fig.height=8, fig.width=8}
up.dn.w2 <- unique(as.character(dmu.w2$Geneid[dmu.w2$up.dn]))
dn.up.w2 <- unique(as.character(dmu.w2$Geneid[dmu.w2$dn.up]))
ll <- unique(c(up.dn.w2,
               dn.up.w2))
# 36 genes

con_uvb_week2 <- data.table(Geneid = res_con_uvb_week2@rownames,
                            log2FoldChange = res_con_uvb_week2@listData$log2FoldChange)

sfn_uvb_week2 <- data.table(Geneid = res_sfn_uvb_week2@rownames,
                            log2FoldChange = -res_sfn_uvb_week2@listData$log2FoldChange)

t1 <- merge(con_uvb_week2[con_uvb_week2$Geneid %in% ll, ],
            sfn_uvb_week2[sfn_uvb_week2$Geneid %in% ll, ],
            by = "Geneid")
colnames(t1)[2:3] <- c("Control vs. UVB",
                       "UVB vs. SFN+UVB")
t1 <- t1[order(t1$`Control vs. UVB`,
               decreasing = TRUE), ]
write.csv(t1,
          file = "tmp/w2_sign_changes.csv",
          row.names = FALSE)

ll <- melt.data.table(data = t1,
                      id.vars = 1,
                      measure.vars = 2:3,
                      variable.name = "Comparison",
                      value.name = "Gene Expression Diff")
ll$Comparison <- factor(ll$Comparison,
                        levels = c("Control vs. UVB",
                                   "UVB vs. SFN+UVB"))
lvls <- ll[ll$Comparison == "Control vs. UVB", ]
ll$Geneid <- factor(ll$Geneid,
                    levels = lvls$Geneid[order(lvls$`Gene Expression Diff`)])

# Add dendrogram----
dt.dndr <- data.frame(t1[Geneid %in% levels(ll$Geneid), ])
rownames(dt.dndr) <- dt.dndr$Gene
dt.dndr <- dt.dndr[, -1]

# Compute distances between genes----
sampleDists <- dist(dt.dndr)

# Make dendrogram data----
dhc <- as.dendrogram(hclust(d = sampleDists),
                     horiz = TRUE)
ddata <- dendro_data(dhc, 
                     type = "rectangle")

# Segment data----
dtp1 <- segment(ddata)

# Hitmap data----
dtp2 <- ll
dtp2$Geneid <- factor(dtp2$Geneid,
                      levels = ddata$labels$label)

offset.size <- 4

p1 <- ggplot(data = dtp2) +
  coord_polar("y",
              start = -0.3,
              direction = -1) +
  geom_tile(aes(x =  as.numeric(Comparison),
                y = Geneid, 
                fill = `Gene Expression Diff`),
            color = "white") +
  geom_text(data = dtp2[Comparison == "Control vs. UVB", ],
            aes(x = rep(1.75,
                        nlevels(Geneid)),
                y = Geneid,
                angle = 90 + seq(from = 30,
                                 to = 330,
                                 length.out = nlevels(Geneid))[as.numeric(Geneid)] + 
                  offset.size,
                label = unique(Geneid)),
            hjust = 0) +
  geom_text(data = dtp2[Geneid == levels(dtp2$Geneid)[1], ],
            aes(x = 1:nlevels(Comparison),
                y = rep(-offset.size,
                        nlevels(Comparison)),
                angle = 0,
                label = levels(Comparison)),
            hjust = 1,
            size = 5) +
  scale_fill_gradient2(low = "red", 
                       high = "green", 
                       mid = "grey", 
                       midpoint = 0, 
                       name = "") +
  scale_y_discrete("",
                   expand = c(0, 0)) +
  theme(plot.title = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.background = element_blank(),
        legend.position = "bottom",
        legend.text = element_text(size = 15),
        legend.direction = "horizontal",
        legend.key.width = unit(1, "in"),
        legend.key.height = unit(0.3, "in")) +
  geom_segment(data = dtp1,
               aes(x = -sqrt(y) + 0.5,
                   y = x, 
                   xend = -sqrt(yend) + 0.5,
                   yend = xend),
               size = 1) 

tiff(filename = "tmp/skin_ubv_w2_sfn_hitmap_with_phylo.tiff",
     height = 8,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)
```

## Venn Diagram, Week 2
```{r w2-venn, fig.height=6,fig.width=4}
# 1. Ctrl vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 1546, 9%
# LFC < 0 (down)     : 1537, 8.9%
# 23 genes down-up-down

# 2. SFN+UVB vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 26, 0.15%
# LFC < 0 (down)     : 35, 0.2%
# 13 gens up-down-up

p1 <- ggplot() +
  geom_circle(aes(x0 = c(1, 2, 1, 2),
                  y0 = c(1, 1, 4, 4),
                  r = rep(1, 4),
                  color = factor(c(2, 1, 1, 2))),
              size = 2) +
  geom_text(aes(x = rep(c(0.5, 1.5, 2.5), 2),
                y = rep(c(1, 4), each = 3),
                label = format(c(26, 13, 35, 1546, 23, 1537),
                               big.mark = ","))) +
  scale_color_manual(values = c("green", "red")) +
  theme_void() +
  theme(legend.position = "none")

tiff(filename = "tmp/skin_ubv_sfn_w2_venn.tiff",
     height = 6,
     width = 4,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)
```

## Effect of UVB at Week 15
```{r deseq2_results_week15_con_uvb}
res_con_uvb_week15 <- results(dds,
                              contrast = c(0,0,0,1,0,1,0,0,0),
                              alpha = 0.1)
res_con_uvb_week15 <- res_con_uvb_week15[order(res_con_uvb_week15$padj,
                                               decreasing = FALSE),]
summary(res_con_uvb_week15)
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 1513, 8.8%
# LFC < 0 (down)     : 1463, 8.5%
# outliers [1]       : 0, 0%
# low counts [2]     : 2668, 16%
# (mean count < 2)

# How many adjusted p-values were less than 0.01?
sum(res_con_uvb_week15$padj < 0.1, 
    na.rm = TRUE)
# 2976

# NOT THE SAME AS?!!!:
res_con_uvb_week15.1 <- results(dds,
                                contrast = list("trt_CON_vs_UVB",
                                                "time15w.trtCON"),
                                alpha = 0.1)
res_con_uvb_week15.1 <- res_con_uvb_week15.1[order(res_con_uvb_week15.1$padj,
                                                   decreasing = FALSE),]
summary(res_con_uvb_week15.1)
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 469, 2.7%
# LFC < 0 (down)     : 455, 2.6%
# outliers [1]       : 0, 0%
# low counts [2]     : 4002, 23%
# (mean count < 6)

# How many adjusted p-values were less than 0.01?
sum(res_con_uvb_week15.1$padj < 0.1, 
    na.rm = TRUE)
# 924

# MA plot
# Save for publication
tiff(filename = "tmp/ma_w15_con_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plotMA(res_con_uvb_week15,
       main = "Control vs. UVB at Week 15",
       alpha = 0.8)
graphics.off()

plotMA(res_con_uvb_week15,
       main = "Control vs. UVB at Week 15",
       alpha = 0.8)
```

## Protective effect of SFN at Week 15
```{r deseq2_results_week15_sfn_uvb}
res_sfn_uvb_week15 <- results(dds,
                              contrast = c(0,0,0,0,1,0,0,1,0),
                              alpha = 0.1)
res_sfn_uvb_week15 <- res_sfn_uvb_week15[order(res_sfn_uvb_week15$padj,
                                               decreasing = FALSE),]
summary(res_sfn_uvb_week15)
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 20, 0.12%
# LFC < 0 (down)     : 10, 0.058%
# outliers [1]       : 0, 0%
# low counts [2]     : 7004, 41%
# (mean count < 53)

# How many adjusted p-values were less than 0.05?
sum(res_sfn_uvb_week15$padj < 0.1, 
    na.rm = TRUE)
# 30

# NOT THE SAME AS!!!:
res_sfn_uvb_week15.1 <- results(dds,
                                contrast = list("trt_SFN_vs_UVB",
                                                "time15w.trtSFN"),
                                alpha = 0.1)
res_sfn_uvb_week15.1 <- res_sfn_uvb_week15.1[order(res_sfn_uvb_week15.1$padj,
                                                   decreasing = FALSE),]
summary(res_sfn_uvb_week15.1)
# out of 17202 with nonzero total read count
# adjusted p-value < 0.1
# LFC > 0 (up)       : 14, 0.081%
# LFC < 0 (down)     : 24, 0.14%
# outliers [1]       : 0, 0%
# low counts [2]     : 3335, 19%
# (mean count < 4)

# How many adjusted p-values were less than 0.1?
sum(res_sfn_uvb_week15.1$padj < 0.1, 
    na.rm = TRUE)
# 38

# MA plot
# Save for publication
tiff(filename = "tmp/ma_w2_sfn_uvb.tiff",
     height = 6,
     width = 7,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(plotMA(res_sfn_uvb_week15,
             main = "UVB+SFN vs UVB at Week 15",
             alpha = 0.8))
graphics.off()

print(plotMA(res_sfn_uvb_week15,
             main = "UVB+SFN vs UVB at Week 15",
             alpha = 0.8))

```

## Genes that were significantly differentiated at both comparisons at Week 15
```{r sign_w15}
lgene.w15.con <- unique(res_con_uvb_week15@rownames[res_con_uvb_week15$padj < 0.1])
lgene.w15.sfn <- unique(res_sfn_uvb_week15@rownames[res_sfn_uvb_week15$padj < 0.1])
lgene.w15 <- lgene.w15.con[lgene.w15.con %in% lgene.w15.sfn]
lgene.w15 <- lgene.w15 [!is.na(lgene.w15 )]
length(unique(lgene.w15))
```
Plot of DESeq-normalizedcounts of genes significant in both comparisons at Week 15:   

```{r deseq2_w15sign_deseqnorm}
# Get the DESeq-normalize counts
dp1 <- list()
for (i in 1:length(lgene.w15)) {
  out <- plotCounts(dds, 
                    gene = lgene.w15[[i]],
                    intgroup = c("trt",
                                 "time"),
                    returnData = TRUE)
  dp1[[i]] <- data.table(Geneid = lgene.w15[[i]],
                         Sample = rownames(out),
                         out)
}
dp1 <- rbindlist(dp1)
dp1$trt <- factor(dp1$trt,
                  levels = c("CON",
                             "UVB",
                             "SFN"))
dp1$time <- factor(dp1$time,
                   levels = c("02w",
                              "15w",
                              "25w"),
                   labels = c("Week 2",
                              "Week 15",
                              "Week 25"))
dp1$Geneid <- factor(dp1$Geneid,
                     levels = lgene.w15)
dp1[, mu := mean(count,
                 na.rm = TRUE),
    by = c("Geneid",
           "trt",
           "time")]
dmu <- unique(dp1[, -c("Sample",
                       "count")])
datatable(head(dmu),
          rownames = FALSE,
          class = "cell-border stripe") %>% 
  formatRound(columns = 4,
              digits = 2)
```

```{r deseq2_w15sign_deseqnorm_w15_up_dn, echo = FALSE, message = FALSE, fig.height = 6, fig.width = 8}
dmu.w15 <- dmu[time == "Week 15", ]
dmu.w15[, up.dn := (mu[trt == "UVB"] > mu[trt == "CON"]) &
               (mu[trt == "UVB"] > mu[trt == "SFN"]),
       by = Geneid]
p1 <- ggplot(dmu.w15[up.dn == TRUE, ],
             aes(x = trt,
                 y = mu,
                 group = Geneid,
                 fill = trt)) +
        facet_wrap(~ Geneid,
                   scale = "free_y") +
        geom_line(position = position_dodge(0.5)) +
        geom_point(position = position_dodge(0.5),
                   shape = 21,
                   size = 3,
                   color = "black") +
        scale_x_discrete("") +
        scale_y_continuous("Differentially Expressed Genes at Week 15") +
        scale_fill_discrete("Treatment") +
        ggtitle("Genes Upregulated by UVB")
theme(axis.text.x = element_text(angle = 45,
                                 hjust = 1))

tiff(filename = "tmp/w15_up_dn.tiff",
     height = 6,
     width = 8,
     units = 'in',
     res = 300,
     compression = "lzw+p")
print(p1)
graphics.off()

print(p1)
```

## Did down-up-down trend persist?
```{r deseq2_w15sign_deseqnorm_plot_all_up_dn, fig.height = 10, fig.width = 12}
dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$up.dn]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$up.dn]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)
```

```{r deseq2_w15sign_deseqnorm_w15_dn_up, echo = FALSE, message = FALSE, fig.height = 6, fig.width = 8}
dmu.w15[, dn.up := (mu[trt == "UVB"] < mu[trt == "CON"]) &
               (mu[trt == "UVB"] < mu[trt == "SFN"]),
       by = Geneid]
p2 <- ggplot(dmu.w15[dn.up == TRUE, ],
             aes(x = trt,
                 y = mu,
                 group = Geneid,
                 fill = trt)) +
        facet_wrap(~ Geneid,
                   scale = "free_y") +
        geom_line(position = position_dodge(0.5)) +
        geom_point(position = position_dodge(0.5),
                   shape = 21,
                   size = 3,
                   color = "black") +
        scale_x_discrete("") +
        scale_y_continuous("Differentially Expressed Genes at Week 15") +
        scale_fill_discrete("Treatment") +
        ggtitle("Genes Downregulated by UVB")
theme(axis.text.x = element_text(angle = 45,
                                 hjust = 1))

tiff(filename = "tmp/w15_dn_up.tiff",
     height = 6,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
print(p2)
graphics.off()

print(p2)
```

## Did up-down-up trend persist?
```{r deseq2_w15sign_deseqnorm_plot_all_dn_up, fig.height = 10, fig.width = 12}
dp1.tmp <- dp1[dp1$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$dn.up]), ]
dmu.tmp <- dmu[dmu$Geneid %in% unique(dmu.w15$Geneid[dmu.w15$dn.up]), ]
p1 <- ggplot(dp1.tmp,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu.tmp,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)
```

## Heatmap for Week 15 differentially methylated genes
```{r w15_heatmap, fig.height=8, fig.width=8}
up.dn.w15 <- unique(as.character(dmu.w15$Geneid[dmu.w15$up.dn]))
dn.up.w15 <- unique(as.character(dmu.w15$Geneid[dmu.w15$dn.up]))
ll <- unique(c(up.dn.w15,
               dn.up.w15))
# 16 genes

con_uvb_week15 <- data.table(Geneid = res_con_uvb_week15@rownames,
                             log2FoldChange = res_con_uvb_week15@listData$log2FoldChange)

sfn_uvb_week15 <- data.table(Geneid = res_sfn_uvb_week15@rownames,
                             log2FoldChange = -res_sfn_uvb_week15@listData$log2FoldChange)

t1 <- merge(con_uvb_week15[con_uvb_week15$Geneid %in% ll, ],
            sfn_uvb_week15[sfn_uvb_week15$Geneid %in% ll, ],
            by = "Geneid")
colnames(t1)[2:3] <- c("Control vs. UVB",
                       "UVB vs. SFN+UVB")
t1 <- t1[order(t1$`Control vs. UVB`,
               decreasing = TRUE), ]
write.csv(t1,
          file = "tmp/w15_sign_changes.csv",
          row.names = FALSE)

ll <- melt.data.table(data = t1,
                      id.vars = 1,
                      measure.vars = 2:3,
                      variable.name = "Comparison",
                      value.name = "Gene Expression Diff")
ll$Comparison <- factor(ll$Comparison,
                        levels = c("Control vs. UVB",
                                   "UVB vs. SFN+UVB"))
lvls <- ll[ll$Comparison == "Control vs. UVB", ]
ll$Geneid <- factor(ll$Geneid,
                    levels = lvls$Geneid[order(lvls$`Gene Expression Diff`)])

# Add dendrogram----
dt.dndr <- data.frame(t1[Geneid %in% levels(ll$Geneid), ])
rownames(dt.dndr) <- dt.dndr$Gene
dt.dndr <- dt.dndr[, -1]

# Compute distances between genes----
sampleDists <- dist(dt.dndr)

# Make dendrogram data----
dhc <- as.dendrogram(hclust(d = sampleDists),
                     horiz = TRUE)
ddata <- dendro_data(dhc, 
                     type = "rectangle")

# Segment data----
dtp1 <- segment(ddata)

# Hitmap data----
dtp2 <- ll
dtp2$Geneid <- factor(dtp2$Geneid,
                      levels = ddata$labels$label)

offset.size <- 4

p1 <- ggplot(data = dtp2) +
  coord_polar("y",
              start = -0.3,
              direction = -1) +
  geom_tile(aes(x =  as.numeric(Comparison),
                y = Geneid, 
                fill = `Gene Expression Diff`),
            color = "white") +
  geom_text(data = dtp2[Comparison == "Control vs. UVB", ],
            aes(x = rep(1.75,
                        nlevels(Geneid)),
                y = Geneid,
                angle = 90 + seq(from = 90,
                                 to = 330,
                                 length.out = nlevels(Geneid))[as.numeric(Geneid)] + 
                  offset.size,
                label = unique(Geneid)),
            hjust = 0) +
  geom_text(data = dtp2[Geneid == levels(dtp2$Geneid)[1], ],
            aes(x = 1:nlevels(Comparison),
                y = rep(-offset.size,
                        nlevels(Comparison)),
                angle = 0,
                label = levels(Comparison)),
            hjust = 1,
            size = 5) +
  scale_fill_gradient2(low = "red", 
                       high = "green", 
                       mid = "grey", 
                       midpoint = 0, 
                       name = "") +
  scale_y_discrete("",
                   expand = c(0, 0)) +
  theme(plot.title = element_text(hjust = 0.5),
        axis.title.x = element_blank(),
        axis.text.x = element_blank(),
        axis.ticks.x = element_blank(),
        axis.title.y = element_blank(),
        axis.text.y = element_blank(),
        axis.ticks.y = element_blank(),
        panel.background = element_blank(),
        legend.position = "bottom",
        legend.text = element_text(size = 15),
        legend.direction = "horizontal",
        legend.key.width = unit(1, "in"),
        legend.key.height = unit(0.3, "in")) +
  geom_segment(data = dtp1,
               aes(x = -sqrt(y) + 0.5,
                   y = x, 
                   xend = -sqrt(yend) + 0.5,
                   yend = xend),
               size = 1) 

tiff(filename = "tmp/skin_ubv_w15_sfn_hitmap_with_phylo.tiff",
     height = 8,
     width = 8,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)
```


## Venn Diagram, Week 15
```{r w15-venn, fig.height=6,fig.width=4}
# out of 17202 with nonzero total read count
# 1. Ctrl vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 1513, 8.8%
# LFC < 0 (down)     : 1463, 8.5%
# 2 genes down-up-down

# 2. SFN+UVB vs. UVB
# adjusted p-value < 0.1
# LFC > 0 (up)       : 20, 0.12%
# LFC < 0 (down)     : 10, 0.058%
# 9 gens up-down-up

p1 <- ggplot() +
  geom_circle(aes(x0 = c(1, 2, 1, 2),
                  y0 = c(1, 1, 4, 4),
                  r = rep(1, 4),
                  color = factor(c(2, 1, 1, 2))),
              size = 2) +
  geom_text(aes(x = rep(c(0.5, 1.5, 2.5), 2),
                y = rep(c(1, 4), each = 3),
                label = format(c(20, 9, 10, 1513, 2, 1463),
                               big.mark = ","))) +
  scale_color_manual(values = c("green", "red")) +
  theme_void() +
  theme(legend.position = "none")

tiff(filename = "tmp/skin_ubv_sfn_w2_venn.tiff",
     height = 6,
     width = 4,
     units = 'in',
     res = 600,
     compression = "lzw+p")
plot(p1)
graphics.off()

print(p1)
```

## Interactions terms
Tests if the effect of NOT treating with UVB vs. treating with UVB is different at Week 15 compared to Week 2:    
```{r deseq2_week2_week15_results_int_con_uvb}
res_int_con_uvb_week <- results(dds, 
                                name = "time15w.trtCON",
                                alpha = 0.1)
res_int_con_uvb_week <- res_int_con_uvb_week[order(res_int_con_uvb_week$padj,
                                                   decreasing = FALSE),]
print(res_int_con_uvb_week)
summary(res_int_con_uvb_week)

# How many adjusted p-values were less than 0.05?
sum(res_int_con_uvb_week$padj < 0.1, 
    na.rm = TRUE)

# MA plot
print(plotMA(res_int_con_uvb_week,
             main = "(Control vs. UVB) x TIme Interaction",
             alpha = 0.9))

```

Tests if the effect of treating with UVB+SFN vs. treating with UVB is different at Week 15 compared to Week 2:    
```{r deseq2_week2_week15_results_int_sfn_uvb}
res_int_sfn_uvb_week <- results(dds, 
                                name = "time15w.trtSFN",
                                alpha = 0.1)
res_int_sfn_uvb_week <- res_int_sfn_uvb_week[order(res_int_sfn_uvb_week$padj,
                                                   decreasing = FALSE),]
print(res_int_sfn_uvb_week)
summary(res_int_sfn_uvb_week)

# How many adjusted p-values were less than 0.05?
sum(res_int_sfn_uvb_week$padj < 0.1, 
    na.rm = TRUE)

# MA plot
print(plotMA(res_int_sfn_uvb_week))

# NOTE: same as 
# res <- results(dds, 
#                   alpha = 0.05)
# res <- res[order(res$padj, decreasing = FALSE),]
# res
```

**NOTE**: By default, the **results(dds)*** prints the results for the last level of the last term, i.e. here it was for for the interaction term SFN vs. UVB at Week 15 vs. Week 2.
       
# Genes with both interactions being significant
```{r sign_int}
lgene.con <- unique(res_int_con_uvb_week@rownames[res_int_con_uvb_week$padj < 0.1])
lgene.sfn <- unique(res_int_sfn_uvb_week@rownames[res_int_sfn_uvb_week$padj < 0.1])
lgene <- lgene.con[lgene.con %in% lgene.sfn]
lgene <- lgene[!is.na(lgene)]
lgene
```

       
Plot of DESeq-normalizedcounts of genes with smallest adjusted p-value for the interaction term:     
```{r deseq2_week2_week15_top9_deseqnorm, fig.height = 6, fig.width = 8}
# Get the DESeq-normalize counts
dp1 <- list()
for (i in 1:length(lgene)) {
  out <- plotCounts(dds, 
                    gene = lgene[[i]],
                    intgroup = c("trt",
                                 "time"),
                    returnData = TRUE)
  dp1[[i]] <- data.table(Geneid = lgene[[i]],
                         Sample = rownames(out),
                         out)
}
dp1 <- rbindlist(dp1)
dp1$trt <- factor(dp1$trt,
                  levels = c("CON",
                             "UVB",
                             "SFN"))
dp1$time <- factor(dp1$time,
                   levels = c("02w",
                              "15w"),
                   labels = c("Week 2",
                              "Week 15"))
dp1$Geneid <- factor(dp1$Geneid,
                     levels = lgene)
dp1[, mu := mean(count,
                 na.rm = TRUE),
    by = c("Geneid",
           "trt",
           "time")]
dmu <- unique(dp1[, -c("Sample",
                       "count")])

p1 <- ggplot(dp1,
             aes(x = time,
                 y = count,
                 group = trt,
                 fill = trt)) +
  facet_wrap(~ Geneid,
             scale = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black") +
  geom_line(data = dmu,
            aes(x = time,
                y = mu,
                group = trt,
                colour = trt),
            position = position_dodge(0.5),
            alpha = 0.5,
            size = 2) +
  scale_x_discrete("") +
  scale_y_continuous("DESeq-Normalized Counts") +
  scale_fill_discrete("Treatment")
print(p1)
```
      
Compare to the plot of TPM-normalizedcounts of genes with smallest adjusted p-value for the interaction term:     
```{r deseq2_week2_week15_tpmnorm, fig.height = 6, fig.width = 8}
# Examine TPM values for the same genes
tmp <- tpm[Geneid %in% lgene, ]
tmp$Geneid <- factor(tmp$Geneid,
                     levels = lgene)
tmp <- melt.data.table(data = tmp,
                       id.vars = 1,
                       measure.vars = 3:ncol(tmp),
                       variable.name = "Sample",
                       value.name = "TPM")
tmp <- merge(dmeta,
             tmp,
             by = "Sample")

p1 <- ggplot(tmp,
             aes(x = Week,
                 y = TPM,
                 fill = Treatment,
                 group = Treatment)) +
  facet_wrap(~ Geneid,
             scales = "free_y") +
  geom_point(position = position_dodge(0.5),
             shape = 21,
             size = 5,
             color = "black")+
  scale_x_discrete("")
plot(p1)
```

# Session Information
```{r info,eval=TRUE}
sessionInfo()
```